Retailers commonly offer tens of thousands of products for sale to the consumers in stores. To offer such a substantial amount of products, retailers require stores of considerable size. To optimize sales, retailers push to keep products in stock by replenishing out-of-stock or limited stock products on store shelves. This way, consumers are able to purchase the product because the product is in stock.
Currently, keeping products in stock is a harrowing task considering the size of stores and the amount of products offered. Conventionally, a fleet of employees manually investigates the store by walking around the store and looking for empty or nearly empty shelves. Checking for stock levels in this manner typically happens only at periodic intervals due to the impracticality of having employees continuously investigating the store. For example, partial restocking efforts typically occur approximately every few hours during the day, with major restocking efforts typically occurring at night, when consumer traffic is lower.
Such conventional techniques are inefficient because the employees are spending a significant amount of time blindly investigating stock levels. As such, employees are prevented from performing other tasks in the store, such as operating checkout stations, or the like. Similarly, employees performing other tasks in the store, such as operating checkout stations, may be required to leave such tasks to perform the blind investigation of stock levels. In addition, because conventional restocking efforts are conducted only on a limited basis, products are more likely to be out-of-stock, thereby hurting sales.
The present invention is aimed at least one or more of the problems identified above.